Experimental Flight Patterns Evaluation for a UAV-Based Air Pollutant Sensor

The use of drones in combination with remote sensors have displayed increasing interest over the last years due to its potential to automate monitoring processes. In this study, a novel approach of a small flying e-nose is proposed by assembling a set of AlphaSense electrochemical-sensors to a DJI M...

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Published inMicromachines (Basel) Vol. 11; no. 8; p. 768
Main Authors Araujo, João Otávio, Valente, João, Kooistra, Lammert, Munniks, Sandra, Peters, Ruud J. B.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 11.08.2020
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Abstract The use of drones in combination with remote sensors have displayed increasing interest over the last years due to its potential to automate monitoring processes. In this study, a novel approach of a small flying e-nose is proposed by assembling a set of AlphaSense electrochemical-sensors to a DJI Matrix 100 unmanned aerial vehicle (UAV). The system was tested on an outdoor field with a source of NO2. Field tests were conducted in a 100 m2 area on two dates with different wind speed levels varying from low (0.0–2.9m/s) to high (2.1–5.3m/s), two flight patterns zigzag and spiral and at three altitudes (3, 6 and 9 m). The objective of this study is to evaluate the sensors responsiveness and performance when subject to distinct flying conditions. A Wilcoxon rank-sum test showed significant difference between flight patterns only under High Wind conditions, with Spiral flights being slightly superior than Zigzag. With the aim of contributing to other studies in the same field, the data used in this analysis will be shared with the scientific community.
AbstractList The use of drones in combination with remote sensors have displayed increasing interest over the last years due to its potential to automate monitoring processes. In this study, a novel approach of a small flying e-nose is proposed by assembling a set of AlphaSense electrochemical-sensors to a DJI Matrix 100 unmanned aerial vehicle (UAV). The system was tested on an outdoor field with a source of NO 2 . Field tests were conducted in a 100 m 2 area on two dates with different wind speed levels varying from low (0.0–2.9m/s) to high (2.1–5.3m/s), two flight patterns zigzag and spiral and at three altitudes (3, 6 and 9 m). The objective of this study is to evaluate the sensors responsiveness and performance when subject to distinct flying conditions. A Wilcoxon rank-sum test showed significant difference between flight patterns only under High Wind conditions, with Spiral flights being slightly superior than Zigzag. With the aim of contributing to other studies in the same field, the data used in this analysis will be shared with the scientific community.
The use of drones in combination with remote sensors have displayed increasing interest over the last years due to its potential to automate monitoring processes. In this study, a novel approach of a small flying e-nose is proposed by assembling a set of AlphaSense electrochemical-sensors to a DJI Matrix 100 unmanned aerial vehicle (UAV). The system was tested on an outdoor field with a source of NO2. Field tests were conducted in a 100 m2 area on two dates with different wind speed levels varying from low (0.0-2.9m/s) to high (2.1-5.3m/s), two flight patterns zigzag and spiral and at three altitudes (3, 6 and 9 m). The objective of this study is to evaluate the sensors responsiveness and performance when subject to distinct flying conditions. A Wilcoxon rank-sum test showed significant difference between flight patterns only under High Wind conditions, with Spiral flights being slightly superior than Zigzag. With the aim of contributing to other studies in the same field, the data used in this analysis will be shared with the scientific community.The use of drones in combination with remote sensors have displayed increasing interest over the last years due to its potential to automate monitoring processes. In this study, a novel approach of a small flying e-nose is proposed by assembling a set of AlphaSense electrochemical-sensors to a DJI Matrix 100 unmanned aerial vehicle (UAV). The system was tested on an outdoor field with a source of NO2. Field tests were conducted in a 100 m2 area on two dates with different wind speed levels varying from low (0.0-2.9m/s) to high (2.1-5.3m/s), two flight patterns zigzag and spiral and at three altitudes (3, 6 and 9 m). The objective of this study is to evaluate the sensors responsiveness and performance when subject to distinct flying conditions. A Wilcoxon rank-sum test showed significant difference between flight patterns only under High Wind conditions, with Spiral flights being slightly superior than Zigzag. With the aim of contributing to other studies in the same field, the data used in this analysis will be shared with the scientific community.
The use of drones in combination with remote sensors have displayed increasing interest over the last years due to its potential to automate monitoring processes. In this study, a novel approach of a small flying e-nose is proposed by assembling a set of AlphaSense electrochemical-sensors to a DJI Matrix 100 unmanned aerial vehicle (UAV). The system was tested on an outdoor field with a source of NO2. Field tests were conducted in a 100 m2 area on two dates with different wind speed levels varying from low (0.0–2.9m/s) to high (2.1–5.3m/s), two flight patterns zigzag and spiral and at three altitudes (3, 6 and 9 m). The objective of this study is to evaluate the sensors responsiveness and performance when subject to distinct flying conditions. A Wilcoxon rank-sum test showed significant difference between flight patterns only under High Wind conditions, with Spiral flights being slightly superior than Zigzag. With the aim of contributing to other studies in the same field, the data used in this analysis will be shared with the scientific community.
Author Valente, João
Peters, Ruud J. B.
Munniks, Sandra
Araujo, João Otávio
Kooistra, Lammert
AuthorAffiliation 1 Information Technology (INF), Wageningen University (WUR), Hollandseweg 1, 6706 KN Wageningen, The Netherlands; joaootavio.araujodasilva@wur.nl
2 Laboratory of Geo-Information Science and Remote Sensing, Wageningen University (WUR), Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands; lammert.kooistra@wur.nl
3 Wageningen Food Safety Research (WFSR), Akkermaalsbos 2, 6708 WB Wageningen, The Netherlands; sandra.munniks@wur.nl (S.M.); ruudj.peters@wur.nl (R.J.B.P.)
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– name: 3 Wageningen Food Safety Research (WFSR), Akkermaalsbos 2, 6708 WB Wageningen, The Netherlands; sandra.munniks@wur.nl (S.M.); ruudj.peters@wur.nl (R.J.B.P.)
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SubjectTerms Air pollution
Chemical sensors
COVID-19
Drone aircraft
electrochemical sensors
Electronic noses
Field tests
Flight
gas sensing
Ground stations
Nitrogen dioxide
Outdoor air quality
Pollutants
Public health
remote sensing
Remote sensors
Satellites
Sensors
unmanned aerial vehicle
Unmanned aerial vehicles
Vehicles
Wind speed
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Title Experimental Flight Patterns Evaluation for a UAV-Based Air Pollutant Sensor
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